Image classification identifying and judging method

Abstract

The invention provides an image classification identifying and judging method. The method includes the steps of firstly, acquiring an image, preprocessing the image, denoising the preprocessed image by a top-hat transformation method and enhancing the denoised image by means of histogram equalization; secondly, subjecting the enhanced image to feature extraction to obtain a joint histogram of an N-dimensional vector, and subjecting the N-dimensional vector to dimensionality reduction, wherein the N is a positive integer; thirdly, training the N-dimensional vector subjected to dimensionality reduction by a training set to summarize a classification set, and extracting a feature image by means of classifying or screening.

Description

technical field [0001] The invention relates to the field of computer image recognition, in particular to an image classification recognition judgment method. Background technique [0002] HSI features are color features, based on the HSI color space established from the perspective of human visual perception. H is chroma, which is an attribute describing pure color; S is saturation, which gives a measure of the degree to which a pure color is diluted by white light; I is brightness, which is the brightness response of vision to light stimulation. [0003] The feature extraction method is to quantify the HSI color space according to the following formula, quantize the chroma H into 8 spaces, divide the saturation S into 3 spaces, and divide the brightness I into 3 spaces, that is, the color space is divided into 72 intervals. [0004] Construct a three-dimensional joint histogram of the three components of H, S, and I, but the HSI feature extraction method has no obvious ef...

AI Technical Summary

Problems solved by technology

[0004] Construct a three-dimensional joint histogram of the three components of H, S, and I, but the HSI feature extraction method has no obvious effect on feature image extraction in a complex image environment.

[0005] In addition, in the case of the SVM classifier, the effect of the complex image environment on the feature image extraction is not obvious, which requires those skilled in the art to continue to solve the corresponding technical problems

[0006] Also, the original image will always have some noise

For example, there are watermarks, uneven lighting, and insufficient lighting make the picture dark, etc.

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